Artificial intelligence is used to find non-obvious strategies and tactics to achieve high-level goals, while having limited ability to affect the world, and limited ability to observe the world. As a result, problem solving in robotics and similar disciplines usually involves control problems. Control problems usually have two parts, an estimation problem, which naturally runs forward in time, and a regulation problem, which naturally runs backwards in time.
Previously, no efficient solution to the fully nonlinear, high-dimensional control problem has been found, although partial solutions exist. Scientists in robotics have solved the nonlinear estimation problem using particle methods. For example, a particle filter with Monte Carlo estimation estimates Bayesian models by representing information, including ambiguity, as a probabilistic distribution. Other techniques have involved linearizing high-dimensional problems to achieve a solution more easily.